What does the RAG technique combine to enhance text generation?

The RAG (Retrieval-Augmented Generation) technique effectively enhances text generation by combining retrieval-based models with generative models. This dual approach allows the system to leverage external knowledge when crafting responses.

Retrieval-based models focus on accessing and utilizing previously stored information or documents, which ensures that the generated content is grounded in factual data and relevant context. By retrieving snippets or entire passages from a knowledge base, the system can incorporate accurate and up-to-date information into the generated text.

On the other hand, generative models are responsible for producing coherent and contextually relevant text based on the input they receive. They excel at creating fluid, human-like responses but may lack the depth of specific knowledge without the assistance of external data sources.

Together, these two components synergize to produce high-quality responses that are not only imaginative but also factually correct, thereby improving the overall quality and reliability of the generated text. This combination makes RAG particularly powerful in applications like chatbots, customer support, and content creation, where accuracy and contextual relevance are critical.